TrendFollowingWorkstheWeakestAfterFinancialCrises

Time-series momentum examines the trend of an asset with respect to its own past performance. This is different than cross-sectional momentum (often referred to as Carhart momentum), which compares the performance of an asset with respect to the performance of another asset.

Research into time-series momentum has found it to be persistent across both time and economic regimes, as well as pervasive across asset classes. It’s also been found to be robust to various definitions. Additionally, it has been shown to be implementable, with little to no evidence of significant capacity constraints.

However, following strong performance in 2008, the aggregate performance of trend-following commodity trading advisor (CTA) funds has been relatively weak. For example, during the period January 2009 to June 2013, the annualized return of the SG CTA Trend Sub-Index (formerly the Newedge CTA Trend Sub-Index) was -0.8%. That’s versus 8.0% over the prior five-year period.

This occurred during a time of slow recovery in the United States and prolonged financial crisis in the eurozone. Relatively poor performance, combined with larger inflows that followed the strong performance, leads investors to ask whether the trend-following strategy will work in the future, or if it has in fact become too crowded.

Trend-Following After A Crisis

Mark Hutchinson and John O’Brien contribute to the body of literature on time-series momentum through their 2014 study, “Is This Time Different? Trend Following and Financial Crises.” Using almost a century of data on trend-following, they investigated what happened to the performance of trend-following subsequent to the U.S. subprime and eurozone crises, and whether it was typical of what happens after a financial crisis.

Hutchinson and O’Brien observed that “identifying a list of global and regional financial crises is problematic.” Thus, they chose to use the list of crises from two of the most highly cited studies of financial crises, “Manias, Panics, and Crashes: A History of Financial Crises” and “This Time Is Different: Eight Centuries of Financial Folly.”

The six global crises the authors studied were: the Great Depression of 1929, the 1973 Oil Crisis, the Third World Debt Crisis of 1981, the Crash of October 1987, the bursting of the dot-com bubble in 2000 and the Sub-Prime/Euro Crisis beginning in 2007. The regional crises studied, with the years of inception in parentheses, were: Spain (1977), Norway (1987), Nordic (1989), Japan (1990), Mexico (1994), Asia (1997), Colombia (1997) and Argentina (2000).

The start date for each crisis was considered to be the month following the equity-market-high preceding the crisis. Because neither of the two aforementioned studies provided guidance on the length or end date of each crisis, rather than attempting to define when every individual crisis ended, the authors instead focused on two fixed time frames—24 months and 48 months—after the prior equity-market-high as their “crisis periods.”

Study Results

Hutchinson and O’Brien’s data set for the global analysis consisted of 21 commodities, 13 government bonds, 21 equity market indexes and currency crosses derived from nine underlying exchange rates covering a sample period from January 1921 to June 2013.

Their results include estimates of trading costs as well as the typical hedge fund fee of 2% of assets and 20% of profits. Following is a summary of their findings:

Time-series momentum has been highly successful over the long term. The average net returns for the global portfolio from 1925 to 2013 was 12.1%, with volatility of 11%. The Sharpe ratio was an impressive 1.1 (a finding consistent with that of other research).

In no-crisis periods, market returns exhibit strong serial correlation at lags of up to 12 months.

Subsequent to a global financial crisis, trend-following performance tends to be weak for four years on average. This lack of time-series return predictability reduces the opportunity for trend-following to generate returns.

Comparing the performance of crisis and no-crisis periods, the average return (4.0%) in the first 24 months after the start of a crisis is less than one-third of the return (13.6%) earned in no-crisis periods. The performance in the 48 months following the start of a crisis (6.0%) was well under half the return in no-crisis periods (14.9%).

Results were consistent across stocks, bonds and currencies. The exception was commodities, where returns were of similar magnitude in pre- as well as post-crisis periods.

The authors found a similar effect when they examined portfolios formed of local assets during regional financial crises.

Hutchinson and O’Brien noted that behavioral models link momentum to investor overconfidence and decreasing risk aversion, with both leading to return predictability in asset prices.

Because, under these models, overconfidence should fall, and risk aversion should increase following market declines, it seems logical that return predictability would fall following a financial crisis.

However, it’s important to note, as the authors did, that “governments have an increased tendency to intervene in financial markets during crises, resulting in discontinuities in price patterns.” Such government interventions can lead to sharp reversals, with the associated negative consequences for trend-following strategies.

The authors concluded that the performance of trend-following strategies is “much weaker in crisis periods, where performance can be as little as one-third of that in normal market conditions.”

They continue, writing: “This result is supported by our evidence for regional crises, though the effect seems to be more short lived. In our analysis of the underlying markets, our empirical evidence indicates a breakdown in the time series predictability, pervasive in normal market conditions, on which trend following relies.”

These findings should give investors confidence that time-series momentum will continue to be a strategy likely to provide diversification benefits and improve portfolio efficiency.

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